New York Times - Articles

Dataset statistics

Number of variables	23
Number of observations	16786
Missing cells	3
Missing cells (%)	< 0.1%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	2.6 MiB
Average record size in memory	165.0 B

Variable types

Categorical	10
Numeric	12
DateTime	1

Alerts

`newsdesk` has a high cardinality: 63 distinct values	High cardinality
`abstract` has a high cardinality: 16543 distinct values	High cardinality
`keywords` has a high cardinality: 15412 distinct values	High cardinality
`uniqueID` has a high cardinality: 16786 distinct values	High cardinality
`lead_paragraph` has a high cardinality: 14994 distinct values	High cardinality
`TEXT_Keywords_POS_PNOUN` is highly correlated with `TEXT_Keywords_ENT_ORG` and 1 other fields	High correlation
`TEXT_Keywords_ENT_ORG` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`TEXT_Keywords_ENT_PERSON` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`TEXT_Keywords_POS_PNOUN` is highly correlated with `TEXT_Keywords_ENT_ORG` and 1 other fields	High correlation
`TEXT_Keywords_ENT_ORG` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`TEXT_Keywords_ENT_PERSON` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`TEXT_Keywords_POS_PNOUN` is highly correlated with `TEXT_Keywords_ENT_ORG` and 1 other fields	High correlation
`TEXT_Keywords_ENT_ORG` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`TEXT_Keywords_ENT_PERSON` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`comment_isHigh` is highly correlated with `newsdesk` and 1 other fields	High correlation
`newsdesk` is highly correlated with `comment_isHigh` and 2 other fields	High correlation
`section` is highly correlated with `comment_isHigh` and 1 other fields	High correlation
`material` is highly correlated with `newsdesk`	High correlation
`newsdesk` is highly correlated with `section` and 7 other fields	High correlation
`section` is highly correlated with `newsdesk` and 6 other fields	High correlation
`material` is highly correlated with `newsdesk` and 5 other fields	High correlation
`n_comments` is highly correlated with `comment_isHigh`	High correlation
`TEXT_Keywords_POS_NOUN` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`TEXT_Keywords_POS_PNOUN` is highly correlated with `TEXT_Keywords_POS_NOUN` and 2 other fields	High correlation
`TEXT_Keywords_ENT_ORG` is highly correlated with `newsdesk` and 2 other fields	High correlation
`TEXT_Keywords_ENT_LOC` is highly correlated with `section`	High correlation
`TEXT_Keywords_ENT_PERSON` is highly correlated with `newsdesk` and 2 other fields	High correlation
`comment_isHigh` is highly correlated with `newsdesk` and 6 other fields	High correlation
`newsdesk_num` is highly correlated with `newsdesk` and 5 other fields	High correlation
`section_num` is highly correlated with `newsdesk` and 5 other fields	High correlation
`material_num` is highly correlated with `newsdesk` and 5 other fields	High correlation
`abstract` is uniformly distributed	Uniform
`uniqueID` is uniformly distributed	Uniform
`uniqueID` has unique values	Unique
`word_count` has 451 (2.7%) zeros	Zeros
`TEXT_Keywords_POS_NOUN` has 2329 (13.9%) zeros	Zeros
`TEXT_Keywords_POS_PNOUN` has 779 (4.6%) zeros	Zeros
`TEXT_Keywords_ENT_ORG` has 5460 (32.5%) zeros	Zeros
`TEXT_Keywords_ENT_GPE` has 8526 (50.8%) zeros	Zeros
`TEXT_Keywords_ENT_LOC` has 16219 (96.6%) zeros	Zeros
`TEXT_Keywords_ENT_PERSON` has 5541 (33.0%) zeros	Zeros

Reproduction

Analysis started	2022-01-29 19:37:34.944937
Analysis finished	2022-01-29 19:38:15.699208
Duration	40.75 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

newsdesk
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct	63
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	131.3 KiB

OpEd	1996
Foreign	1073
Culture	1049
Business	981
Washington	843
Other values (58)	10844

Length

Max length	20
Median length	7
Mean length	7.054390564
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	< 0.1%

Sample

1st row	U.S.
2nd row	OpEd
3rd row	OpEd
4th row	OpEd
5th row	OpEd

Common Values

Value	Count	Frequency (%)
OpEd	1996	11.9%
Foreign	1073	6.4%
Culture	1049	6.2%
Business	981	5.8%
Washington	843	5.0%
Metro	820	4.9%
Science	750	4.5%
National	715	4.3%
Learning	691	4.1%
RealEstate	682	4.1%
Other values (53)	7186	42.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
oped	1996	11.8%
foreign	1073	6.4%
culture	1049	6.2%
business	990	5.9%
washington	843	5.0%
metro	820	4.9%
science	750	4.4%
national	715	4.2%
learning	695	4.1%
realestate	682	4.0%
Other values (56)	7269	43.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

section
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	42
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	131.3 KiB

U.S.	2364
Opinion	2272
World	1183
Arts	1093
New York	1055
Other values (37)	8819

Length

Max length	20
Median length	7
Mean length	7.501668057
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	< 0.1%

Sample

1st row	U.S.
2nd row	Opinion
3rd row	Opinion
4th row	Opinion
5th row	Opinion

Common Values

Value	Count	Frequency (%)
U.S.	2364	14.1%
Opinion	2272	13.5%
World	1183	7.0%
Arts	1093	6.5%
New York	1055	6.3%
Business Day	932	5.6%
The Learning Network	708	4.2%
Real Estate	687	4.1%
Well	630	3.8%
Food	573	3.4%
Other values (32)	5289	31.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
u.s	2364	10.5%
opinion	2272	10.1%
world	1183	5.2%
arts	1093	4.8%
new	1055	4.7%
york	1055	4.7%
the	1052	4.7%
business	932	4.1%
day	932	4.1%
learning	708	3.1%
Other values (45)	9912	43.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

material
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	10
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	131.3 KiB

News	13088
Op-Ed	2053
Review	537
Interactive Feature	451
briefing	240
Other values (5)	417

Length

Max length	19
Median length	4
Mean length	4.852972715
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Interactive Feature
2nd row	Op-Ed
3rd row	Op-Ed
4th row	Op-Ed
5th row	Op-Ed

Common Values

Value	Count	Frequency (%)
News	13088	78.0%
Op-Ed	2053	12.2%
Review	537	3.2%
Interactive Feature	451	2.7%
briefing	240	1.4%
Obituary (Obit)	194	1.2%
Editorial	159	0.9%
News Analysis	59	0.4%
Letter	3	< 0.1%
List	2	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
news	13147	75.2%
op-ed	2053	11.7%
review	537	3.1%
interactive	451	2.6%
feature	451	2.6%
briefing	240	1.4%
obituary	194	1.1%
obit	194	1.1%
editorial	159	0.9%
analysis	59	0.3%
Other values (2)	5	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

abstract
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	16543
Distinct (%)	98.6%
Missing	3
Missing (%)	< 0.1%
Memory size	131.3 KiB

The latest on stock market and business news during the coronavirus outbreak.	43
Look closely at this image, stripped of its caption, and join the moderated conversation about what you and other students see.	31
Teenage comments in response to our recent writing prompts, and an invitation to join the ongoing conversation.	28
Recent residential sales in New York City and the region.	24
A look at one of the entries from last week’s puzzles that stumped our solvers.	20
Other values (16538)	16637

Length

Max length	626
Median length	131
Mean length	127.3022702
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	16511 ?
Unique (%)	98.4%

Sample

1st row	The Times obtained Donald Trump’s tax information extending over more than two decades, revealing struggling properties, vast write-offs, an audit battle and hundreds of millions in debt coming due.
2nd row	It has to do with respect.
3rd row	Tara Reade’s allegations against Joe Biden demand action.
4th row	The goal would be to renew faith in our government, but its effect would be the opposite.
5th row	The competition among his cabineteers is fierce.

Common Values

Value	Count	Frequency (%)
The latest on stock market and business news during the coronavirus outbreak.	43	0.3%
Look closely at this image, stripped of its caption, and join the moderated conversation about what you and other students see.	31	0.2%
Teenage comments in response to our recent writing prompts, and an invitation to join the ongoing conversation.	28	0.2%
Recent residential sales in New York City and the region.	24	0.1%
A look at one of the entries from last week’s puzzles that stumped our solvers.	20	0.1%
What is this image saying?	14	0.1%
Live updates on stock market and business news during the coronavirus outbreak.	14	0.1%
What story does this image inspire for you?	13	0.1%
Our columnists and contributors give their rankings.	13	0.1%
Appointment viewing is back. Find out what online events to look for today, and when to tune in.	11	0.1%
Other values (16533)	16572	98.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	20996	6.0%
a	10474	3.0%
to	9685	2.8%
of	9148	2.6%
and	9128	2.6%
in	7416	2.1%
for	3571	1.0%
is	3210	0.9%
are	2784	0.8%
on	2757	0.8%
Other values (29100)	272839	77.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

keywords
Categorical

HIGH CARDINALITY

Distinct	15412
Distinct (%)	91.8%
Missing	0
Missing (%)	0.0%
Memory size	131.3 KiB

[]	720
['Crossword Puzzles']	206
['Coronavirus (2019-nCoV)']	159
['New York City']	52
['Customs, Etiquette and Manners']	21
Other values (15407)	15628

Length

Max length	1385
Median length	160
Mean length	170.9187418
Min length	2

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	15314 ?
Unique (%)	91.2%

Sample

1st row	['Trump, Donald J', 'Trump Tax Returns', 'Conflicts of Interest', 'Trump Organization', 'Presidential Election of 2020', 'Tax Credits, Deductions and Exemptions', 'Income Tax', 'United States Politics and Government', 'Lobbying and Lobbyists', 'Miss Universe Organization', 'Consultants', 'Real Estate (Commercial)', 'Real Estate and Housing (Residential)', 'Hotels and Travel Lodgings', 'Golf']
2nd row	['United States Politics and Government', 'Presidential Election of 2020', 'Presidents and Presidency (US)', 'Immigration and Emigration', 'Health Insurance and Managed Care', 'Illegal Immigration', 'Hydraulic Fracturing', 'Polls and Public Opinion', 'Voting and Voters', 'Democratic Party', 'Sanders, Bernard', 'Trump, Donald J', 'Liberalism (US Politics)', 'Conservatism (US Politics)']
3rd row	['Biden, Joseph R Jr', 'Reade, Tara', 'Sex Crimes', '#MeToo Movement', 'Presidential Election of 2020', "Women's Rights", 'Democratic National Committee', 'Democratic Party', 'Democratic National Convention']
4th row	['United States Politics and Government', 'Presidential Election of 2020', 'Constitution (US)', 'Law and Legislation', 'Justice Department', 'Trump, Donald J', 'Trump Tax Returns', 'Trump-Ukraine Whistle-blower Complaint and Impeachment Inquiry', 'Russian Interference in 2016 US Elections and Ties to Trump Associates', 'Presidents and Presidency (US)']
5th row	['United States Politics and Government', 'Azar, Alex M II', 'Barr, William P', 'Chao, Elaine L', 'DeVos, Elizabeth (1958- )', 'Lighthizer, Robert E', 'Pence, Mike', 'Pompeo, Mike', 'Ross, Wilbur L Jr', 'Trump, Donald J', 'Wheeler, Andrew R', 'Wolf, Chad F', 'DeJoy, Louis', 'Bernhardt, David L']

Common Values

Value	Count	Frequency (%)
[]	720	4.3%
['Crossword Puzzles']	206	1.2%
['Coronavirus (2019-nCoV)']	159	0.9%
['New York City']	52	0.3%
['Customs, Etiquette and Manners']	21	0.1%
['Coronavirus (2019-nCoV)', 'New York State', 'New York City']	19	0.1%
['New York City', 'Coronavirus (2019-nCoV)', 'Cuomo, Andrew M', 'de Blasio, Bill', 'New York State', 'Deaths (Fatalities)', 'Hospitals']	14	0.1%
['George Floyd Protests (2020)']	12	0.1%
['Presidential Election of 2020']	10	0.1%
['Civilian Casualties', 'Taliban', 'AFGHANISTAN', 'Afghan National Security Forces', 'Kabul (Afghanistan)']	8	< 0.1%
Other values (15402)	15565	92.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
and	26482	8.0%
coronavirus	6579	2.0%
states	5520	1.7%
2019-ncov	5380	1.6%
united	5342	1.6%
of	4678	1.4%
politics	3661	1.1%
government	3628	1.1%
j	3141	0.9%
	3137	0.9%
Other values (18919)	264393	79.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

word_count
Real number (ℝ_≥0)

ZEROS

Distinct	3035
Distinct (%)	18.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1300.961813

Minimum	0
Maximum	15619
Zeros	451
Zeros (%)	2.7%
Negative	0
Negative (%)	0.0%
Memory size	131.3 KiB

Quantile statistics

Minimum	0
5-th percentile	230
Q1	874
median	1183
Q3	1502
95-th percentile	2658.75
Maximum	15619
Range	15619
Interquartile range (IQR)	628

Descriptive statistics

Standard deviation	944.8018613
Coefficient of variation (CV)	0.7262333541
Kurtosis	23.89830344
Mean	1300.961813
Median Absolute Deviation (MAD)	312.5
Skewness	3.733577943
Sum	21837945
Variance	892650.5571
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	451	2.7%
1255	32	0.2%
1352	26	0.2%
1150	26	0.2%
1190	26	0.2%
893	26	0.2%
917	25	0.1%
1279	25	0.1%
1224	23	0.1%
1277	23	0.1%
Other values (3025)	16103	95.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	451	2.7%
4	1	< 0.1%
10	1	< 0.1%
16	4	< 0.1%
36	1	< 0.1%
37	1	< 0.1%
38	1	< 0.1%
43	1	< 0.1%
46	1	< 0.1%
55	1	< 0.1%

Value	Count	Frequency (%)
15619	1	< 0.1%
12947	1	< 0.1%
11538	1	< 0.1%
11409	1	< 0.1%
11084	1	< 0.1%
11020	1	< 0.1%
10708	1	< 0.1%
10496	1	< 0.1%
10329	1	< 0.1%
9781	1	< 0.1%

pub_date
Date

Distinct	15399
Distinct (%)	91.7%
Missing	0
Missing (%)	0.0%
Memory size	131.3 KiB

Minimum	2020-01-01 00:18:54+00:00
Maximum	2020-12-31 18:02:02+00:00

Histogram

Histogram with fixed size bins (bins=50)

n_comments
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	1905
Distinct (%)	11.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	297.0567139

Minimum	1
Maximum	8987
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	131.3 KiB

Quantile statistics

Minimum	1
5-th percentile	3
Q1	21
median	87
Q3	323
95-th percentile	1358
Maximum	8987
Range	8986
Interquartile range (IQR)	302

Descriptive statistics

Standard deviation	513.4191755
Coefficient of variation (CV)	1.728354053
Kurtosis	18.51319882
Mean	297.0567139
Median Absolute Deviation (MAD)	79
Skewness	3.470217812
Sum	4986394
Variance	263599.2498
Monotonicity	Decreasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2	295	1.8%
1	292	1.7%
3	281	1.7%
5	281	1.7%
6	252	1.5%
4	247	1.5%
7	246	1.5%
8	230	1.4%
10	212	1.3%
12	205	1.2%
Other values (1895)	14245	84.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	292	1.7%
2	295	1.8%
3	281	1.7%
4	247	1.5%
5	281	1.7%
6	252	1.5%
7	246	1.5%
8	230	1.4%
9	205	1.2%
10	212	1.3%

Value	Count	Frequency (%)
8987	1	< 0.1%
5702	1	< 0.1%
5228	1	< 0.1%
4760	1	< 0.1%
4718	1	< 0.1%
4687	1	< 0.1%
4610	1	< 0.1%
4595	1	< 0.1%
4532	1	< 0.1%
4486	1	< 0.1%

uniqueID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	16786
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	131.3 KiB

nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75	1
nyt://article/f48d3fc2-53ea-5fa5-a182-bf641a501563	1
nyt://article/682f745a-d103-5647-9c1f-bf96567b01be	1
nyt://article/e547a0c1-6f1f-5674-9a55-42e0c727968f	1
nyt://article/4642a70d-45d8-5102-bfaf-f141bc451480	1
Other values (16781)	16781

Length

Max length	54
Median length	50
Mean length	50.10747051
Min length	50

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	16786 ?
Unique (%)	100.0%

Sample

1st row	nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75
2nd row	nyt://article/57736deb-c3d4-5640-8732-bce6442e16c4
3rd row	nyt://article/0583f62d-570d-50fd-acbd-7d0b78c50aaf
4th row	nyt://article/7e29ac1e-fd93-5e9f-ab32-55ef8947ccbd
5th row	nyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c7

Common Values

Value	Count	Frequency (%)
nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75	1	< 0.1%
nyt://article/f48d3fc2-53ea-5fa5-a182-bf641a501563	1	< 0.1%
nyt://article/682f745a-d103-5647-9c1f-bf96567b01be	1	< 0.1%
nyt://article/e547a0c1-6f1f-5674-9a55-42e0c727968f	1	< 0.1%
nyt://article/4642a70d-45d8-5102-bfaf-f141bc451480	1	< 0.1%
nyt://article/ef5dfc4e-de9b-5935-a108-1ae44dc32ac7	1	< 0.1%
nyt://article/9d101233-1569-5433-9b0d-4da761da2efb	1	< 0.1%
nyt://article/38693f15-3b09-5857-9792-ce468c394f4e	1	< 0.1%
nyt://article/965642d3-0029-5faf-bfcc-4fc1fa026557	1	< 0.1%
nyt://article/8ab11dea-c795-5a1d-ab04-2c8206c326fc	1	< 0.1%
Other values (16776)	16776	99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75	1	< 0.1%
nyt://article/4a3415ef-4390-577c-9cde-438b8b10bfd7	1	< 0.1%
nyt://article/a3d78b17-7bbc-52d1-9d0d-0250fb632dab	1	< 0.1%
nyt://article/b781b70e-f02b-5bc1-b789-0194db88714b	1	< 0.1%
nyt://article/0583f62d-570d-50fd-acbd-7d0b78c50aaf	1	< 0.1%
nyt://article/7e29ac1e-fd93-5e9f-ab32-55ef8947ccbd	1	< 0.1%
nyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c7	1	< 0.1%
nyt://article/120696c7-aa80-5be0-b7da-0649b449c913	1	< 0.1%
nyt://article/d35b554e-eda8-505f-80b0-3e9b9344ecd1	1	< 0.1%
nyt://article/4974c21a-f80b-5d19-96e4-fed6eb0a7bbd	1	< 0.1%
Other values (16776)	16776	99.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

lead_paragraph
Categorical

HIGH CARDINALITY

Distinct	14994
Distinct (%)	89.3%
Missing	0
Missing (%)	0.0%
Memory size	131.3 KiB

[Want to get New York Today by email? Here’s the sign-up.]	229
Listen and subscribe to our podcast from your mobile device:Via Apple Podcasts \| Via Spotify \| Via Stitcher	209
	191
Times Insider explains who we are and what we do, and delivers behind-the-scenes insights into how our journalism comes together.	179
Welcome to Best of Late Night, a rundown of the previous night’s highlights that lets you sleep — and lets us get paid to watch comedy. We’re all stuck at home at the moment, so here are the 50 best movies on Netflix right now.	88
Other values (14989)	15890

Length

Max length	1805
Median length	228
Mean length	241.3827594
Min length	0

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	14813 ?
Unique (%)	88.2%

Sample

1st row	The Times obtained Donald Trump’s tax information extending over more than two decades, revealing struggling properties, vast write-offs, an audit battle and hundreds of millions in debt coming due.
2nd row	The last four presidents — Bill Clinton, George W. Bush, Barack Obama and Donald Trump — are four very different politicians. But they have one crucial similarity: They all tried to appeal to voters who weren’t obvious supporters.
3rd row	If you’re lucky when you report your sexual assault, you’ll become known as a person who was sexually assaulted. If you’re unlucky, you’ll become known as a person who lied about being sexually assaulted.
4th row	As the Biden administration slowly coalesces, there have been many calls for its Justice Department to prosecute Donald Trump for any crimes he may have committed while in office. The hope, proponents of this view argue, is to establish that the president is subject to the rule of law and to deter future presidents from breaking the law.
5th row	OK, people, I know you’re feeling a little wan and helpless these days. Sure does seem like a long time until November.

Common Values

Value	Count	Frequency (%)
[Want to get New York Today by email? Here’s the sign-up.]	229	1.4%
Listen and subscribe to our podcast from your mobile device:Via Apple Podcasts \| Via Spotify \| Via Stitcher	209	1.2%
	191	1.1%
Times Insider explains who we are and what we do, and delivers behind-the-scenes insights into how our journalism comes together.	179	1.1%
Welcome to Best of Late Night, a rundown of the previous night’s highlights that lets you sleep — and lets us get paid to watch comedy. We’re all stuck at home at the moment, so here are the 50 best movies on Netflix right now.	88	0.5%
Click on the slide show to see this week’s featured properties:	70	0.4%
Dear Diary:	52	0.3%
Welcome to Best of Late Night, a rundown of the previous night’s highlights that lets you sleep — and lets us get paid to watch comedy. If you’re interested in hearing from The Times regularly about great TV, sign up for our Watching newsletter and get recommendations straight to your inbox.	48	0.3%
To hear more audio stories from publishers like The New York Times, download Audm for iPhone or Android.	39	0.2%
This briefing is no longer updating. Read the latest developments in the coronavirus outbreak here.	31	0.2%
Other values (14984)	15650	93.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	38183	5.6%
a	19789	2.9%
of	19263	2.8%
and	17098	2.5%
to	16898	2.5%
in	14777	2.2%
on	6856	1.0%
that	6581	1.0%
for	6477	1.0%
—	6344	0.9%
Other values (44608)	527964	77.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

month
Real number (ℝ_≥0)

Distinct	12
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.320743477

Minimum	1
Maximum	12
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	131.3 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	3
median	6
Q3	9
95-th percentile	12
Maximum	12
Range	11
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	3.43705575
Coefficient of variation (CV)	0.5437739662
Kurtosis	-1.210922789
Mean	6.320743477
Median Absolute Deviation (MAD)	3
Skewness	0.09747293216
Sum	106100
Variance	11.81335223
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
4	1666	9.9%
3	1644	9.8%
5	1538	9.2%
1	1443	8.6%
10	1415	8.4%
12	1338	8.0%
7	1317	7.8%
2	1312	7.8%
9	1307	7.8%
6	1306	7.8%
Other values (2)	2500	14.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	1443	8.6%
2	1312	7.8%
3	1644	9.8%
4	1666	9.9%
5	1538	9.2%
6	1306	7.8%
7	1317	7.8%
8	1259	7.5%
9	1307	7.8%
10	1415	8.4%

Value	Count	Frequency (%)
12	1338	8.0%
11	1241	7.4%
10	1415	8.4%
9	1307	7.8%
8	1259	7.5%
7	1317	7.8%
6	1306	7.8%
5	1538	9.2%
4	1666	9.9%
3	1644	9.8%

TEXT_Keywords_POS_NOUN
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	23
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.038067437

Minimum	0
Maximum	23
Zeros	2329
Zeros (%)	13.9%
Negative	0
Negative (%)	0.0%
Memory size	131.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	3
Q3	4
95-th percentile	7
Maximum	23
Range	23
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.411232217
Coefficient of variation (CV)	0.7936730395
Kurtosis	2.313830009
Mean	3.038067437
Median Absolute Deviation (MAD)	2
Skewness	1.111502034
Sum	50997
Variance	5.814040805
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=23)

Value	Count	Frequency (%)
2	2933	17.5%
3	2772	16.5%
1	2668	15.9%
0	2329	13.9%
4	2080	12.4%
5	1534	9.1%
6	1020	6.1%
7	638	3.8%
8	360	2.1%
9	192	1.1%
Other values (13)	260	1.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	2329	13.9%
1	2668	15.9%
2	2933	17.5%
3	2772	16.5%
4	2080	12.4%
5	1534	9.1%
6	1020	6.1%
7	638	3.8%
8	360	2.1%
9	192	1.1%

Value	Count	Frequency (%)
23	1	< 0.1%
21	2	< 0.1%
20	1	< 0.1%
19	1	< 0.1%
18	2	< 0.1%
17	7	< 0.1%
16	1	< 0.1%
15	5	< 0.1%
14	8	< 0.1%
13	20	0.1%

TEXT_Keywords_POS_PNOUN
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	89
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	13.80900751

Minimum	0
Maximum	146
Zeros	779
Zeros (%)	4.6%
Negative	0
Negative (%)	0.0%
Memory size	131.3 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	7
median	12
Q3	19
95-th percentile	31
Maximum	146
Range	146
Interquartile range (IQR)	12

Descriptive statistics

Standard deviation	9.945965656
Coefficient of variation (CV)	0.7202520276
Kurtosis	10.37938259
Mean	13.80900751
Median Absolute Deviation (MAD)	6
Skewness	1.859670049
Sum	231798
Variance	98.92223283
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
10	839	5.0%
9	817	4.9%
8	805	4.8%
11	789	4.7%
6	788	4.7%
0	779	4.6%
12	766	4.6%
7	733	4.4%
14	707	4.2%
2	691	4.1%
Other values (79)	9072	54.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	779	4.6%
1	91	0.5%
2	691	4.1%
3	412	2.5%
4	520	3.1%
5	640	3.8%
6	788	4.7%
7	733	4.4%
8	805	4.8%
9	817	4.9%

Value	Count	Frequency (%)
146	1	< 0.1%
132	1	< 0.1%
119	1	< 0.1%
113	2	< 0.1%
112	1	< 0.1%
111	1	< 0.1%
110	1	< 0.1%
106	1	< 0.1%
105	1	< 0.1%
102	1	< 0.1%

TEXT_Keywords_ENT_ORG
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	18
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.514416776

Minimum	0
Maximum	18
Zeros	5460
Zeros (%)	32.5%
Negative	0
Negative (%)	0.0%
Memory size	131.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	5
Maximum	18
Range	18
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.617063671
Coefficient of variation (CV)	1.067779819
Kurtosis	4.262192957
Mean	1.514416776
Median Absolute Deviation (MAD)	1
Skewness	1.577332615
Sum	25421
Variance	2.614894915
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=18)

Value	Count	Frequency (%)
0	5460	32.5%
1	4513	26.9%
2	3114	18.6%
3	1880	11.2%
4	911	5.4%
5	465	2.8%
6	240	1.4%
7	106	0.6%
8	46	0.3%
9	29	0.2%
Other values (8)	22	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	5460	32.5%
1	4513	26.9%
2	3114	18.6%
3	1880	11.2%
4	911	5.4%
5	465	2.8%
6	240	1.4%
7	106	0.6%
8	46	0.3%
9	29	0.2%

Value	Count	Frequency (%)
18	1	< 0.1%
16	1	< 0.1%
15	1	< 0.1%
14	1	< 0.1%
13	1	< 0.1%
12	4	< 0.1%
11	6	< 0.1%
10	7	< 0.1%
9	29	0.2%
8	46	0.3%

TEXT_Keywords_ENT_NORP
Categorical

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	131.3 KiB

0	15585
1	1123
2	72
3	6

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	15585	92.8%
1	1123	6.7%
2	72	0.4%
3	6	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	15585	92.8%
1	1123	6.7%
2	72	0.4%
3	6	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

TEXT_Keywords_ENT_FAC
Categorical

Distinct	5
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	131.3 KiB

0	16557
1	199
2	27
3	2
4	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	16557	98.6%
1	199	1.2%
2	27	0.2%
3	2	< 0.1%
4	1	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	16557	98.6%
1	199	1.2%
2	27	0.2%
3	2	< 0.1%
4	1	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

TEXT_Keywords_ENT_GPE
Real number (ℝ_≥0)

ZEROS

Distinct	18
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.8454664601

Minimum	0
Maximum	37
Zeros	8526
Zeros (%)	50.8%
Negative	0
Negative (%)	0.0%
Memory size	131.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	3
Maximum	37
Range	37
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.218004638
Coefficient of variation (CV)	1.440630345
Kurtosis	63.77050509
Mean	0.8454664601
Median Absolute Deviation (MAD)	0
Skewness	4.131694158
Sum	14192
Variance	1.483535299
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=18)

Value	Count	Frequency (%)
0	8526	50.8%
1	4757	28.3%
2	2147	12.8%
3	815	4.9%
4	323	1.9%
5	104	0.6%
6	52	0.3%
7	29	0.2%
8	12	0.1%
9	6	< 0.1%
Other values (8)	15	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	8526	50.8%
1	4757	28.3%
2	2147	12.8%
3	815	4.9%
4	323	1.9%
5	104	0.6%
6	52	0.3%
7	29	0.2%
8	12	0.1%
9	6	< 0.1%

Value	Count	Frequency (%)
37	1	< 0.1%
24	1	< 0.1%
15	2	< 0.1%
14	2	< 0.1%
13	1	< 0.1%
12	1	< 0.1%
11	5	< 0.1%
10	2	< 0.1%
9	6	< 0.1%
8	12	0.1%

TEXT_Keywords_ENT_LOC
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	6
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.03872274514

Minimum	0
Maximum	5
Zeros	16219
Zeros (%)	96.6%
Negative	0
Negative (%)	0.0%
Memory size	131.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	5
Range	5
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.2224823015
Coefficient of variation (CV)	5.745519866
Kurtosis	69.56136531
Mean	0.03872274514
Median Absolute Deviation (MAD)	0
Skewness	7.177187438
Sum	650
Variance	0.04949837448
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	16219	96.6%
1	499	3.0%
2	57	0.3%
3	8	< 0.1%
4	2	< 0.1%
5	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	16219	96.6%
1	499	3.0%
2	57	0.3%
3	8	< 0.1%
4	2	< 0.1%
5	1	< 0.1%

Value	Count	Frequency (%)
5	1	< 0.1%
4	2	< 0.1%
3	8	< 0.1%
2	57	0.3%
1	499	3.0%
0	16219	96.6%

TEXT_Keywords_ENT_PERSON
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	26
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.864768259

Minimum	0
Maximum	40
Zeros	5541
Zeros (%)	33.0%
Negative	0
Negative (%)	0.0%
Memory size	131.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	3
95-th percentile	6
Maximum	40
Range	40
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.277985511
Coefficient of variation (CV)	1.221591745
Kurtosis	13.0983628
Mean	1.864768259
Median Absolute Deviation (MAD)	1
Skewness	2.461893008
Sum	31302
Variance	5.189217989
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=26)

Value	Count	Frequency (%)
0	5541	33.0%
1	4009	23.9%
2	2612	15.6%
3	1597	9.5%
4	1197	7.1%
5	669	4.0%
6	457	2.7%
7	249	1.5%
8	150	0.9%
9	91	0.5%
Other values (16)	214	1.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	5541	33.0%
1	4009	23.9%
2	2612	15.6%
3	1597	9.5%
4	1197	7.1%
5	669	4.0%
6	457	2.7%
7	249	1.5%
8	150	0.9%
9	91	0.5%

Value	Count	Frequency (%)
40	1	< 0.1%
30	1	< 0.1%
24	1	< 0.1%
23	1	< 0.1%
22	1	< 0.1%
20	1	< 0.1%
19	1	< 0.1%
18	7	< 0.1%
17	5	< 0.1%
16	7	< 0.1%

comment_isHigh
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	16.6 KiB

0	12838
1	3948

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
0	12838	76.5%
1	3948	23.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	12838	76.5%
1	3948	23.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

newsdesk_num
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	63
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	30.14738473

Minimum	0
Maximum	62
Zeros	8
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	65.7 KiB

Quantile statistics

Minimum	0
5-th percentile	6
Q1	14
median	33
Q3	41
95-th percentile	60
Maximum	62
Range	62
Interquartile range (IQR)	27

Descriptive statistics

Standard deviation	17.11737567
Coefficient of variation (CV)	0.5677897379
Kurtosis	-1.014975335
Mean	30.14738473
Median Absolute Deviation (MAD)	13
Skewness	0.1496767391
Sum	506054
Variance	293.0045499
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
34	1996	11.9%
14	1073	6.4%
9	1049	6.2%
6	981	5.8%
59	843	5.0%
25	820	4.9%
41	750	4.5%
30	715	4.3%
21	691	4.1%
40	682	4.1%
Other values (53)	7186	42.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	8	< 0.1%
1	266	1.6%
2	2	< 0.1%
3	6	< 0.1%
4	195	1.2%
5	54	0.3%
6	981	5.8%
7	9	0.1%
8	145	0.9%
9	1049	6.2%

Value	Count	Frequency (%)
62	20	0.1%
61	615	3.7%
60	213	1.3%
59	843	5.0%
58	8	< 0.1%
57	280	1.7%
56	11	0.1%
55	105	0.6%
54	205	1.2%
53	52	0.3%

section_num
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	42
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	22.02764208

Minimum	0
Maximum	41
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	65.7 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	14
median	20
Q3	35
95-th percentile	40
Maximum	41
Range	41
Interquartile range (IQR)	21

Descriptive statistics

Standard deviation	12.01067238
Coefficient of variation (CV)	0.5452545641
Kurtosis	-1.154425339
Mean	22.02764208
Median Absolute Deviation (MAD)	10
Skewness	-0.06340488543
Sum	369756
Variance	144.2562511
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=42)

Value	Count	Frequency (%)
36	2364	14.1%
18	2272	13.5%
40	1183	7.0%
1	1093	6.5%
16	1055	6.3%
6	932	5.6%
30	708	4.2%
22	687	4.1%
39	630	3.8%
11	573	3.4%
Other values (32)	5289	31.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	< 0.1%
1	1093	6.5%
2	5	< 0.1%
3	1	< 0.1%
4	371	2.2%
5	3	< 0.1%
6	932	5.6%
7	148	0.9%
8	468	2.8%
9	7	< 0.1%

Value	Count	Frequency (%)
41	4	< 0.1%
40	1183	7.0%
39	630	3.8%
38	3	< 0.1%
37	5	< 0.1%
36	2364	14.1%
35	213	1.3%
34	115	0.7%
33	245	1.5%
32	6	< 0.1%

material_num
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	10
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.474025974

Minimum	0
Maximum	9
Zeros	159
Zeros (%)	0.9%
Negative	0
Negative (%)	0.0%
Memory size	65.7 KiB

Quantile statistics

Minimum	0
5-th percentile	4
Q1	4
median	4
Q3	4
95-th percentile	7
Maximum	9
Range	9
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	1.479664063
Coefficient of variation (CV)	0.3307231722
Kurtosis	1.954423472
Mean	4.474025974
Median Absolute Deviation (MAD)	0
Skewness	0.8273478618
Sum	75101
Variance	2.189405739
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
4	13088	78.0%
7	2053	12.2%
8	537	3.2%
1	451	2.7%
9	240	1.4%
6	194	1.2%
0	159	0.9%
5	59	0.4%
2	3	< 0.1%
3	2	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	159	0.9%
1	451	2.7%
2	3	< 0.1%
3	2	< 0.1%
4	13088	78.0%
5	59	0.4%
6	194	1.2%
7	2053	12.2%
8	537	3.2%
9	240	1.4%

Value	Count	Frequency (%)
9	240	1.4%
8	537	3.2%
7	2053	12.2%
6	194	1.2%
5	59	0.4%
4	13088	78.0%
3	2	< 0.1%
2	3	< 0.1%
1	451	2.7%
0	159	0.9%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	newsdesk	section	material	abstract	keywords	word_count	pub_date	n_comments	uniqueID	lead_paragraph	month	TEXT_Keywords_POS_NOUN	TEXT_Keywords_POS_PNOUN	TEXT_Keywords_ENT_ORG	TEXT_Keywords_ENT_NORP	TEXT_Keywords_ENT_FAC	TEXT_Keywords_ENT_GPE	TEXT_Keywords_ENT_LOC	TEXT_Keywords_ENT_PERSON	comment_isHigh	newsdesk_num	section_num	material_num
0	U.S.	U.S.	Interactive Feature	The Times obtained Donald Trump’s tax information extending over more than two decades, revealing struggling properties, vast write-offs, an audit battle and hundreds of millions in debt coming due.	['Trump, Donald J', 'Trump Tax Returns', 'Conflicts of Interest', 'Trump Organization', 'Presidential Election of 2020', 'Tax Credits, Deductions and Exemptions', 'Income Tax', 'United States Politics and Government', 'Lobbying and Lobbyists', 'Miss Universe Organization', 'Consultants', 'Real Estate (Commercial)', 'Real Estate and Housing (Residential)', 'Hotels and Travel Lodgings', 'Golf']	0	2020-09-27 21:07:33+00:00	8987	nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75	The Times obtained Donald Trump’s tax information extending over more than two decades, revealing struggling properties, vast write-offs, an audit battle and hundreds of millions in debt coming due.	9	9	28	5	0	0	0	0	3	1	55	36	1
1	OpEd	Opinion	Op-Ed	It has to do with respect.	['United States Politics and Government', 'Presidential Election of 2020', 'Presidents and Presidency (US)', 'Immigration and Emigration', 'Health Insurance and Managed Care', 'Illegal Immigration', 'Hydraulic Fracturing', 'Polls and Public Opinion', 'Voting and Voters', 'Democratic Party', 'Sanders, Bernard', 'Trump, Donald J', 'Liberalism (US Politics)', 'Conservatism (US Politics)']	934	2020-02-23 23:33:29+00:00	5702	nyt://article/57736deb-c3d4-5640-8732-bce6442e16c4	The last four presidents — Bill Clinton, George W. Bush, Barack Obama and Donald Trump — are four very different politicians. But they have one crucial similarity: They all tried to appeal to voters who weren’t obvious supporters.	2	7	30	2	0	0	1	0	3	1	34	18	7
2	OpEd	Opinion	Op-Ed	Tara Reade’s allegations against Joe Biden demand action.	['Biden, Joseph R Jr', 'Reade, Tara', 'Sex Crimes', '#MeToo Movement', 'Presidential Election of 2020', "Women's Rights", 'Democratic National Committee', 'Democratic Party', 'Democratic National Convention']	986	2020-05-03 19:00:07+00:00	5228	nyt://article/0583f62d-570d-50fd-acbd-7d0b78c50aaf	If you’re lucky when you report your sexual assault, you’ll become known as a person who was sexually assaulted. If you’re unlucky, you’ll become known as a person who lied about being sexually assaulted.	5	4	15	3	0	0	0	0	2	1	34	18	7
3	OpEd	Opinion	Op-Ed	The goal would be to renew faith in our government, but its effect would be the opposite.	['United States Politics and Government', 'Presidential Election of 2020', 'Constitution (US)', 'Law and Legislation', 'Justice Department', 'Trump, Donald J', 'Trump Tax Returns', 'Trump-Ukraine Whistle-blower Complaint and Impeachment Inquiry', 'Russian Interference in 2016 US Elections and Ties to Trump Associates', 'Presidents and Presidency (US)']	984	2020-12-03 10:00:12+00:00	4760	nyt://article/7e29ac1e-fd93-5e9f-ab32-55ef8947ccbd	As the Biden administration slowly coalesces, there have been many calls for its Justice Department to prosecute Donald Trump for any crimes he may have committed while in office. The hope, proponents of this view argue, is to establish that the president is subject to the rule of law and to deter future presidents from breaking the law.	12	4	30	3	0	0	1	0	2	1	34	18	7
4	OpEd	Opinion	Op-Ed	The competition among his cabineteers is fierce.	['United States Politics and Government', 'Azar, Alex M II', 'Barr, William P', 'Chao, Elaine L', 'DeVos, Elizabeth (1958- )', 'Lighthizer, Robert E', 'Pence, Mike', 'Pompeo, Mike', 'Ross, Wilbur L Jr', 'Trump, Donald J', 'Wheeler, Andrew R', 'Wolf, Chad F', 'DeJoy, Louis', 'Bernhardt, David L']	886	2020-08-05 23:40:48+00:00	4718	nyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c7	OK, people, I know you’re feeling a little wan and helpless these days. Sure does seem like a long time until November.	8	0	44	2	1	0	1	0	16	1	34	18	7
5	Metro	New York	News	Video of the incident touched off intense discussions about the history of black people being falsely reported to the police.	['Black People', 'Blacks', 'Central Park (Manhattan, NY)', 'Race and Ethnicity', 'Social Media', 'Cooper, Amy (May 25, 2020 Central Park Incident)', 'Discrimination', 'Dogs', 'Birdwatching', 'Cooper, Christian (Birder)', 'Video Recordings, Downloads and Streaming']	1211	2020-05-26 12:13:27+00:00	4687	nyt://article/120696c7-aa80-5be0-b7da-0649b449c913	[The latest: Amy Cooper, the white woman in Central Park who called the police on a Black bird-watcher, will be charged with filing a false report.]	5	7	19	2	1	0	1	1	0	1	25	16	4
6	OpEd	Opinion	Op-Ed	He is not a liberal, he’s the end of liberalism.	['Sanders, Bernard', 'Democratic Party', 'Presidential Election of 2020', 'Liberalism (US Politics)', 'United States Politics and Government', 'Trump, Donald J']	888	2020-02-28 00:00:07+00:00	4610	nyt://article/d35b554e-eda8-505f-80b0-3e9b9344ecd1	A few months ago, I wrote a column saying I would vote for Elizabeth Warren over Donald Trump. I may not agree with some of her policies, but culture is more important than politics. She does not spread moral rot the way Trump does.	2	1	14	2	0	0	0	0	3	1	34	18	7
7	Washington	World	News	Suleimani was planning attacks on Americans across the region, leading to an airstrike in Baghdad, the Pentagon statement said. Iran’s supreme leader called for vengeance.	['Iran', 'Suleimani, Qassim', 'Defense and Military Forces', 'Quds Force', 'Islamic Revolutionary Guards Corps', 'United States International Relations', 'United States Defense and Military Forces', 'Targeted Killings', 'Drones (Pilotless Planes)', 'Trump, Donald J', 'Baghdad International Airport (Iraq)', 'Iraq', 'Popular Mobilization Forces (Iraq)']	2333	2020-01-03 01:02:54+00:00	4595	nyt://article/4974c21a-f80b-5d19-96e4-fed6eb0a7bbd	WEST PALM BEACH, Fla. — Iran’s top security and intelligence commander was killed early Friday in a drone strike at Baghdad International Airport that was authorized by President Trump, American officials said.	1	3	35	3	0	1	2	0	3	1	59	40	4
8	Washington	U.S.	News	An examination reveals the president was warned about the potential for a pandemic but that internal divisions, lack of planning and his faith in his own instincts led to a halting response.	['Coronavirus (2019-nCoV)', 'Trump, Donald J', 'United States Politics and Government', 'United States Economy', 'United States International Relations', 'Presidential Election of 2020', 'Shutdowns (Institutional)', 'Quarantines', 'China', 'Travel Warnings', 'Centers for Disease Control and Prevention', 'National Institutes of Health', 'National Security Council', 'Health and Human Services Department', 'Azar, Alex M II', 'Birx, Deborah L', 'Fauci, Anthony S', 'Mnuchin, Steven T', 'Navarro, Peter', "O'Brien, Robert C (1952- )", 'Pence, Mike', 'Pottinger, Matthew', 'Redfield, Robert R']	5356	2020-04-11 16:13:09+00:00	4532	nyt://article/0c53a478-7276-5fe7-abb7-e9272ed0f06b		4	6	58	8	0	0	1	0	13	1	59	36	4
9	National	U.S.	News	Police officers used flash grenades to disperse a crowd so the president could visit for a photo opportunity. And in New York, protesters and looters defied a curfew.	['Police Brutality, Misconduct and Shootings', 'Demonstrations, Protests and Riots', 'United States', 'Minneapolis (Minn)', 'George Floyd Protests (2020)']	3787	2020-06-01 09:13:44+00:00	4486	nyt://article/b316ddae-bf03-5de8-b9e6-a796d0471430	[Follow our live coverage of the George Floyd protests.]	6	1	13	0	1	0	2	0	1	1	30	36	4

Last rows

	newsdesk	section	material	abstract	keywords	word_count	pub_date	n_comments	uniqueID	lead_paragraph	month	TEXT_Keywords_POS_NOUN	TEXT_Keywords_POS_PNOUN	TEXT_Keywords_ENT_ORG	TEXT_Keywords_ENT_NORP	TEXT_Keywords_ENT_GPE	TEXT_Keywords_ENT_LOC	TEXT_Keywords_ENT_PERSON	newsdesk_num	section_num	material_num
16776	Learning	The Learning Network	News	In this lesson, students will take a virtual field trip through some of the world’s greatest museums.	[]	605	2020-04-30 13:31:57+00:00	1	nyt://article/14d2dd3b-d670-5801-af70-8ff2db7f4b2f	Featured Article: “Now Virtual and in Video, Museum Websites Shake Off the Dust” by Jason Farago	4	0	0	0	0	0	0	0	21	30	4
16777	Culture	Arts	News	Appointment viewing is back. Find out what online events to look for today, and when to tune in.	['Cinco de Mayo', 'Music', 'Television', 'Theater', 'Quarantine (Life and Culture)', 'Dancing', 'Coronavirus (2019-nCoV)', 'Video Recordings, Downloads and Streaming', 'Adlon, Pamela', 'Cantone, Mario', 'Longoria, Eva', 'Grammy Museum', 'New York City Ballet', 'Ninety-Second Street Y', 'Valenzuela, Francisca (1987- )', 'Santos, Enrique (Music Executive)']	427	2020-05-05 09:00:33+00:00	1	nyt://article/5e3a69b5-5ccb-597f-822f-f9d6e2c889af	Here are a few of the best events happening on Tuesday and how to tune in (all times are Eastern).	5	7	31	3	0	1	0	4	9	1	4
16778	Culture	Arts	News	Yotam Haber’s “Estro Poetico-Armonico III” combines live singing with archival recordings of cantors.	['Jews and Judaism', 'Classical Music', 'Azrieli Foundation', 'Awards, Decorations and Honors', 'Haber, Yotam', 'Estro Poetico-Armonico III (Musical Work)']	1011	2020-10-20 14:47:43+00:00	1	nyt://article/35b54def-309e-52a3-8c82-6a5133e0f6ca	Since early in his career, Yotam Haber has grappled with what it means to be a contemporary Jewish composer. The tentative answers offered by his music — full of allusions, distortion and whispers of the past — suggest that the grappling itself is a vital part of that identity.	10	3	14	2	0	1	0	2	9	1	4
16779	Podcasts	Podcasts	News	Our interview with a longtime protester about her journey to the front lines.	['The Daily (Radio Program)', 'Podcasts', 'Newsletters', 'New York Times', 'Elections', 'Presidential Election of 2020', 'Race and Ethnicity', 'Police Brutality, Misconduct and Shootings', 'Black Lives Matter Movement', 'Demonstrations, Protests and Riots', 'George Floyd Protests (2020)']	926	2020-08-07 22:10:23+00:00	1	nyt://article/b00bc7b4-64c1-575b-8753-6097f0c3f300	Producer Sydney Harper on Wednesday’s episode:	8	5	19	1	1	0	0	2	38	20	4
16780	Styles	Style	News	Rachel Comey and Vaquera get New York Fashion Week off to a buzzy start.	['Fashion and Apparel', 'New York Fashion Week', 'Fashion Shows', 'Rag & Bone (Fashion Label)', 'Burch, Tory', 'Maxwell, Brandon (1984- )', 'Rogers, Christopher John', 'Comey, Rachel', 'your-feed-fashion']	1167	2020-02-10 16:18:52+00:00	1	nyt://article/003acab0-8d5d-5dd7-905e-4ac90f9d706e	New York Fashion Week dribbled into existence just after President Trump’s impeachment acquittal victory lap, and in the shadow of the Bernie-Buttigieg squabbles over Iowa. Not to mention the Oscars.	2	3	20	1	0	0	0	4	46	26	4
16781	Sports	Sports	News	Computer-vs.-computer games of FIFA livestream to gamblers on Twitch. Fantasy contests carry League of Legends lineups. In the coronavirus age, video games have grown into a darling for casinos.	['E-Sports', 'Casinos', 'Computer and Video Games', 'Coronavirus (2019-nCoV)', 'Regulation and Deregulation of Industry', 'Nevada Gaming Control Board', 'Gambling', 'Quarantine (Life and Culture)']	1955	2020-06-08 07:00:22+00:00	1	nyt://article/159dda5b-2815-51ba-ad15-00bcc9c73dfd	Marco Blume, trading director for the sports book Pinnacle, remembers when betting on the competitive video games known as e-sports was an exotic concept.	6	4	14	2	0	0	0	1	44	25	4
16782	SpecialSections	Arts	News	Public monuments, and the artists who create them, are beginning to represent women and their achievements.	['Monuments and Memorials (Structures)', 'Women and Girls', "Women's Rights", 'Sculpture', 'Art', 'Bergmann, Meredith (Artist)', 'Central Park (Manhattan, NY)', 'Kentucky', 'Matthews, Amanda (1968- )']	1077	2020-10-21 09:00:32+00:00	1	nyt://article/ab567633-47cb-56ff-8cf0-29b49e2873f3	This article is part of our latest Fine Arts & Exhibits special report, which focuses on how art endures and inspires, even in the darkest of times.	10	7	13	3	0	2	1	1	43	1	4
16783	SpecialSections	Arts	News	The visitors may be masked, but the art is gradually coming into full view.	['Museums', 'Coronavirus Reopenings', 'AMERICAN MUSEUM OF NATURAL HISTORY', 'Dallas Museum of Art', 'Gardner, Isabella Stewart, Museum', 'Los Angeles County Museum of Art', 'Metropolitan Museum of Art', 'Museum of Fine Arts (Boston)', 'Museum of Modern Art', 'National Gallery of Art', 'Whitney Museum of American Art', 'Perez, Jorge M, Art Museum of Miami-Dade County', 'Museum of Fine Arts (Houston)', 'Cleveland Museum of Art', 'Sirmans, Franklin', 'Tinterow, Gary', 'Weinberg, Adam D', 'Zumthor, Peter']	1642	2020-10-21 09:00:29+00:00	1	nyt://article/a7c443c8-1e8f-5426-b6d6-54347c448ece	This article is part of our latest Fine Arts & Exhibits special report, which focuses on how art endures and inspires, even in the darkest of times.	10	2	57	9	1	2	0	6	43	1	4
16784	NewsDesk	Arts	News	Plus, the road to women’s suffrage, finding a new normal and more live Times events this week.	[]	1000	2020-05-18 09:00:10+00:00	1	nyt://article/ef863416-0249-5def-a51b-ae7bd6ed3b32	The coronavirus has transformed nearly every aspect of daily life, but Times journalists are here to keep you informed and connected from home. Below is a selection of our live events this week (all times are Eastern). You can find the full calendar here.	5	0	0	0	0	0	0	0	32	1	4
16785	Obits	Obituaries	News	A member of the Black Panthers, he helped lead a historic, and successful, sit-in in San Francisco as part of a nationwide anti-discrimination campaign on behalf of people with disabilities.	['Demonstrations, Protests and Riots', 'Disabilities', 'Black People', 'Blacks', 'Discrimination', 'Black Panther Party', 'Califano, Joseph A Jr', 'Heumann, Judith (1947- )', 'California', 'AMERICANS WITH DISABILITIES ACT', 'Lomax, Brad (1950-84)']	1376	2020-07-08 20:48:22+00:00	1	nyt://article/5b1bf30b-7f4c-5c15-9910-55217ddd2b82	Overlooked is a series of obituaries about remarkable people whose deaths, beginning in 1851, went unreported in The Times. This latest installment is part of a series exploring how the Americans With Disabilities Act has shaped modern life for disabled people. Share your stories or email us at ada@nytimes.com.	7	5	18	1	1	1	0	4	33	17	4

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